Propofol for Treatment of Acute Migraine in the Emergency Department: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Propofol has not been extensively studied as an acute migraine therapy; however, based on the limited evidence from outpatient and inpatient settings, propofol has been proposed as an option for patients who present to the emergency department (ED). The purpose of this review was to evaluate the existing literature regarding the safety and efficacy of propofol for acute migraine treatment in the ED. METHODS: A systematic review of clinical studies of propofol treatment for acute migraine in the ED was performed using Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Trials were identified through PubMed, Google Scholar, clinical trial registries, research registries, and key journals through May 2019. A modified Jadad scoring system was used to assess the methodologic quality of the included randomized controlled trials, and the Newcastle-Ottawa Scale was used for the retrospective cohort study. RESULTS: Nine studies, including five case reports or series, one retrospective cohort study, and three randomized controlled trials, consisting of 290 patients, were reviewed. All studies in adults reported propofol to be an effective therapy for migraine, but the strength of these results was limited by dosing variations, small sample sizes, and limited generalizability. Pediatric studies produced mixed results. CONCLUSIONS: Propofol may be an effective rescue therapy for patients presenting to the ED for acute migraine, but its place in therapy based on the limited available evidence is unknown. The safety of propofol for migraine management in the ED has not been adequately examined.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".